STATE OF MICHIGAN DEPARTMENT OF NATURAL RESOURCES Relationships Between Habitat and

DNR
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PARTMEN
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STATE OF MICHIGAN
DEPARTMENT OF NATURAL RESOURCES
RR2091
July 2009
Relationships Between Habitat and
Fish Density in Michigan Streams
Troy G. Zorn, Paul W. Seelbach, and Michael J. Wiley
www.michigan.gov/dnr/
FISHERIES DIVISION
RESEARCH REPORT 2091
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MICHIGAN DEPARTMENT OF NATURAL RESOURCES
FISHERIES DIVISION
Fisheries Research Report 2091
July 2009
Relationships Between Habitat and Fish Density
in Michigan Streams
Troy G. Zorn,
Paul W. Seelbach,
and
Michael J. Wiley
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Suggested Citation Format
Zorn, T. G., P. W. Seelbach, and M. J. Wiley. 2009. Relationships between habitat and fish
density in Michigan streams. Michigan Department of Natural Resources, Fisheries Research
Report 2091, Ann Arbor.
Michigan Department of Natural Resources
Fisheries Research Report 2091, 2009
Relationships Between Habitat and Fish Density in Michigan Streams
Troy G. Zorn
Michigan Department of Natural Resources, Marquette Fisheries Research Station
484 Cherry Creek Road, Marquette, Michigan 49855
Paul W. Seelbach
Michigan Department of Natural Resources, Institute for Fisheries Research
212 Museums Annex Building, Ann Arbor, MI 48109-1084
Michael J. Wiley
University of Michigan, School of Natural Resources and Environment
Ann Arbor, Michigan 48109-1115
Abstract.–We developed simple decision support tools (plots) for fishery managers in
Michigan that are based on habitat data and fish population estimates for several hundred stream
sites throughout the state. We generated contour plots to show patterns in fish biomass for over 60
common species (and 120 species grouped at the family level) in relation to axes of catchment
area (CA) and low-flow yield (LFY; 90% exceedance flow divided by CA), and then against axes
of mean and weekly range in July temperature. The plots showed distinct patterns in fish density
at each level of biological organization studied and were useful for quantitatively comparing river
sites. Contour plots were also made for fish assemblage attributes such as species richness and
total density. We demonstrated how these plots can be used to support stream management and
provided examples pertaining to resource assessment, trout stocking, angling regulations,
chemical reclamation of marginal trout streams, indicator species, instream flow protection, and
habitat restoration. These tools are electronically available, so managers can easily access and
incorporate them into decision protocols and presentations.
Introduction
Management of stream fisheries at the local scale would benefit from decision support tools (i.e.,
quantitative fish-habitat relationships) derived from data collected locally or regionally. Existing
regional or national datasets, such as Habitat Suitability Index models (e.g., Raleigh et al. 1986) may
lack samples for a particular stream type or include such a broad array of hydrologic types that the
resolution of the data is inadequate for supporting local-scale decisions. For example, a national
sample of trout streams (e.g., Poff and Ward 1989) may include rivers where habitat conditions are
driven by mountain elevations, snowmelt, hydropower dam flow releases, or groundwater inputs,
though only a subset of these factors may significantly influence streams in a particular region (e.g.,
groundwater inputs are key in glaciated Midwestern states). On the other hand, fish-habitat
relationships (models) from detailed, location-specific studies may be difficult to apply to other
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regions if stream conditions differ (Fausch et al. 1988) or if making predictions of fish density
requires additional resources (e.g., software, technical expertise, funding, etc.). Though much fishery
management occurs by state agencies, state-scale summaries relating fish density and habitat data are
often lacking, not standardized (gear specific), or have yet to be synthesized at the state scale.
Through the Michigan Rivers Inventory (MRI) project (Seelbach and Wiley 1997), data have
been collected for describing aquatic assemblages and habitat at several hundred sites in the state.
These data have been used to develop models for understanding and classifying systems (Seelbach
et al. 1997; Wiley and Seelbach 1997; Zorn et al. 2002). The models have also enabled prediction of
streamflow characteristics (Wiley and Seelbach, unpublished data), summer water temperatures
(Wehrly et al. 1997), and fish assemblages (Zorn et al. 2004) for the state’s rivers. The MRI database
would readily lend itself to development of simple decision support tools (e.g., plots) relating fish
density to habitat, but such synthesis was lacking.
Previous studies have demonstrated that spatial patterns in fish distribution and abundance in
glaciated Midwestern streams can largely be accounted for by relatively few habitat variables. The
importance of stream size, measured as catchment area, is both well known and documented (e.g.,
Hynes 1972; Lyons 1996; Zorn et al. 2002). Low-flow yield, defined as 90% exceedance flow
divided by catchment area, is a measure of groundwater contribution to streams and an index of
important parameters such as stream temperature, hydrologic stability, and current velocity
(Hendrickson and Doonan 1972; Poff and Allan 1995; Zorn et al. 2002). Summer temperature is one
of the major factors affecting growth (Brett 1979), survival (Smale and Rabeni 1995a), and
distribution of fish (Magnuson et al. 1979; Smale and Rabeni 1995b; Lyons 1996; Wehrly et al. 2003;
Zorn et al. 2004) throughout the Midwest and worldwide. The objective of this study was to develop
simple decision support tools for Michigan biologists that relate key habitat variables to densities of
commonly occurring fish species, species grouped at the genus or family level, and other groupings
deemed useful to fishery managers.
Methods
Study Area
Data were obtained for this study from several hundred stream sites scattered across Michigan.
The entire state was influenced by Pleistocene glaciation, and except for portions of the Upper
Peninsula, it is covered by unconsolidated glacial deposits ranging in texture from coarse sands and
gravels associated with moraines and glacial outwash, to clays from former glacial lakes. The
thickness of these deposits ranges from a few feet to several hundred feet. The texture, depth, and
associated hydrologic properties of these deposits have a strong influence on river flow, channel
conditions, and fish assemblages (Hendrickson and Doonan 1972; Zorn et al. 2002).
Data Sources
Fisheries survey data were obtained for 332 stream sites in the Lower Peninsula from the MRI
database (Seelbach and Wiley 1997), and 46 sites on Upper Peninsula waters from a companion study
to the MRI (Baker 2006). Surveys were conducted in wadeable stream reaches during summer from
1982 to 2001. Density estimates were available for the entire fish assemblage at 298 sites sampled via
rotenone or multi-pass electrofishing depletion surveys, and mark-recapture estimates for salmonids
were obtained at an additional 80 sites (Figure 1). Seelbach and Wiley (1997) and Seelbach et al.
(1988) provide greater detail regarding fish sampling techniques and computation of abundance
estimates.
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The large number of sites with fish density data provided an excellent sample of Michigan
streams, with a few caveats. Small streams (i.e., having catchment areas < 10 mi2) and Upper
Peninsula waters were somewhat undersampled, given their abundance on the landscape (Figure 1).
Fish density estimates from rotenone surveys may represent only about 75% of actual values because
of sampling inefficiency (Seelbach et al. 1994). To make density estimates of all species captured, we
assumed equal catchability of all fishes at electrofishing depletion sites (Zippen 1958), but there was
undoubtedly variation in catchability among species. While no replicate samples occurred at specific
sites, the overall data captured temporal variation to some degree by covering a broad sampling
period. So, although any individual sample may not perfectly represent a site’s typical fish
assemblage, the existence of fish density data from the several hundred sites essentially provided
replicate samples for many types of Michigan rivers. In addition, we expected density patterns for
most species at the statewide scale to be dramatic enough (e.g., contrasts of high versus low versus
zero density levels) that sampling induced biases would not significantly alter our findings.
Three types of stream habitat data were used for this study. Catchment area (CA) was measured
for each site using geographic information system techniques. Ninety percent exceedance flow values
were obtained from the same sources as the fish survey data and consisted of a combination of
measurements from United States Geological Survey gauging stations and regression model
predictions (Seelbach and Wiley, unpublished data and Baker 2006). Low-flow yield (LFY) was
computed for a site by dividing its 90% annual exceedance flow by its catchment area. July stream
temperature data were obtained for 379 sites, and consisted of hourly measurements at the vast
majority of sites, and weekly maximum and minimum readings at others. From these data we
computed July mean temperature which (depending upon the data source), was the average of the
hourly readings or an average of the weekly readings. To determine the comparability of July mean
temperature values calculated from these two types of data, we used hourly temperature data from a
subsample of sites and compared July mean temperature values computed with both methods. We
found that these two calculations produced values that were nearly identical (r = 0.995). We also
computed July weekly temperature range which was the average of the differences between each
week’s maximum and minimum temperatures. July temperature values were predicted at sites where
measurements were not available (Wehrly et al. 1997).
Data Analysis
The data were analyzed several ways to depict relationships between species density and habitat.
We generated contour plots to show patterns in fish biomass, for species and select groups of taxa
(Table 1) in relation to axes of LFY and CA, and then against axes of mean and weekly range in July
temperature. This was accomplished by describing subsets of MRI sites that met particular LFY, CA,
or temperature criteria. We developed sampling matrices, with sites grouped into cells according to
their LFY, CA, or temperature values (Figure 2). Average values for fish density and these habitat
parameters were calculated from MRI data available for the subset of sites in each cell, and plotted on
these axes. This analysis was also done for numerical density of salmonids since they are of particular
management interest in Michigan.
We think that plots of fish abundances on these habitat axes may reflect long-term average
population levels, since abundances were averaged from many similar sites sampled during different
years. Population estimates from individual fish surveys may differ considerably from these values
because of natural fluctuations in population levels. For example, replicate rotenone samples
available from seven warmwater stream sites (Zorn, unpublished data) showed up to three-fold
differences in abundance levels of the more common species (i.e., those having abundances >10
kg/ha). Wiley et al. (1997) suggested that 15 to 20 years of population estimate data may be needed to
accurately characterize the long-term mean and variance of trout populations in hydrologically-stable
(groundwater-fed) Michigan streams. Since such long-term data do not exist for most Michigan
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streams, pooling similar sites allowed us to develop initial estimates of the mean and variance in fish
populations associated with different stream conditions.
We produced scatterplots of numerical density of brown trout and brook trout against July mean
temperature for Michigan streams. The wedge-shaped distribution of data relating July mean
temperature to trout density indicated that July mean temperature is an index of conditions that
become limiting to trout (Terrell et al. 1996; Thompson et al. 1996). To demonstrate this relation, we
visually fit a line along the upper portion of the data to show the relation between maximum
(potential) brown trout density and July mean temperature.
We developed a spreadsheet model that described how close conditions of a site were to what is
optimal for 68 common fishes in Michigan rivers. First, we standardized (Z-distribution, mean = 0,
SD = 1) the fish density data by species. For each species, we selected sites where it was relatively
abundant (z-score > 0.75), hereafter referring to them as “optimal” sites, and computed the mean and
standard deviation for their LFY, CA, and mean July temperature values. For each species, the
spreadsheet model assigned scores to the site’s LFY, CA, and mean July temperature conditions
based upon the number of SD’s these values were away from “optimal” LFY, CA, and mean July
temperature values for the species. The site received a 4, 3, 2, or 1 score if its values were within 0.5,
1.0, 1.5, or 2.0 standard deviations of the optimal values for a species; a 0 score was given if the site’s
value was more than 2.0 standard deviations from the species’ optima value. Composite scores were
calculated for each modeled species at a site as the minimum of the three individual variable scores,
implying that any one of the three variables (or factors correlated with them) may limit species
density at a site. This is justifiable because these habitat variables are tied to aspects of fish habitat
important to fish metabolism, survival, and reproductive success (e.g., temperature, dissolved oxygen,
current velocity and aeration, depth, permanence of habitats, etc.). An average of the individual
variable scores was also computed for each species.
Results
Our Michigan-based habitat suitability models were based upon a broad array of river conditions.
The MRI sites studied had attribute values ranging over several orders of magnitude. For example,
catchment areas ranged from 0.4 to 5513 mi2 (stream widths from 2 to 350 feet), low-flow yields
from 0.0008 to 2.93 cubic feet per second per square mile (ft3*s-1*mi-2), July mean temperatures from
48 to 80°F, and July weekly temperature range values were between 4 and 31ºF. Low-flow yield and
catchment area were closely tied to July mean temperature (Figure 3). Total fish density at sites
ranged from 5 to 1004 pounds per acre and species richness varied from 1 to 40 (Appendix A).
The plots are useful for distinguishing habitat affinities among species, comparing river systems,
and assessing potential response of systems to various management activities. Some species such as
brook trout and smallmouth bass have fairly restricted stream size and hydrology “preferences,"
whereas other fishes (e.g., white sucker and rock bass) can do well under a broader array of
conditions and abundance peaks are not as distinctive (Figure 4). Similar patterns can be seen at
higher taxonomic levels, with salmonids being most abundant in rivers with high groundwater inputs,
dace becoming more abundant as LFY values decrease, and suckers and catfishes being more
prominent in larger rivers with lower LFY’s (Figure 5). Narrow versus broad habitat tolerances could
be distinguished among species, and the plots provided a useful means for assessing the suitability of
a given set of conditions for species of management interest (Figure 6). For example, opportunities
are being explored for reducing the downstream thermal effects of a millpond on the Middle Branch
River, a tributary to the Muskegon River (O’Neal 2006). Conditions upstream of the millpond
indicated that the impounded river reach and area downstream would have excellent potential for
supporting substantial populations of coldwater fishes if warming effects of the impoundment were
eliminated (Figure 6).
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Our data showed that mean July temperature (or one of its correlates) can limit a stream’s
potential to support brown trout density, since maximum fish densities observed generally declined
with increasing temperature (Figure 7). Such information can be used to assess the potential of waters
for different types of management (e.g., stocking, protective regulations, etc.). For example,
contrasting thermal conditions (and resulting coldwater fishery potential) in heavily stocked tailwaters
of the Au Sable, Manistee, and Muskegon rivers may allow for different management approaches
(Figure 7). Minimum size limits for brown trout are lower in waters (i.e., Muskegon River below
Croton Dam) where water temperatures are typically warm and annual survival of trout is relatively
low (Michigan Department of Natural Resources Fisheries Division, MDNR-FD, unpublished data).
Higher size limits (and a trophy trout fishery) appear more feasible in the Au Sable River below Mio
Dam, which is often cooler and has better trout survival (MDNR-FD, unpublished data). A similar
relationship appears to occur between mean July temperature and brook trout density (Figure 8).
The above results provided just a few examples of the utility of these plots. Their main value,
however, is in supporting fisheries management decision-making at the local level. A complete set of
plots is provided to help achieve this objective (Appendix A). The plots are also available in
electronic format via the Michigan Department of Natural Resources intranet site or can be obtained
by contacting the lead author of this report. To facilitate comparisons among rivers, the LFY, CA, and
July temperature values for sites with fish density estimates used in this study can also be obtained
from the same sources.
The spreadsheet model provided a useful means for assessing the suitability of sites for different
fishes. Optimal LFY, CA, and July temperature values for each species allowed for quantitative
comparisons of differences in habitat preference among species (e.g., Table 2). For example, plots of
optimal CA and mean July temperatures for each species show a progression from species typical of
cold- and cool-water small streams to those characteristic of large, warm rivers (Figure 9). Highly
ranked species, based on composite suitability scores, from model test runs for the Huron River (a
large, warmwater river) and Hunt Creek (a small, trout stream) reasonably corroborated unpublished
MDNR-FD survey data on fish assemblage structure for these waters (Table 2 and Table 3). Hunt
Creek, like many inland streams, does not have Great Lakes salmonids but was rated highly for them
because Great Lakes accessibility was not a model parameter.
Discussion
Management Applications
This analysis fills a basic need of fishery managers, namely to have regionally based, data-rich,
simple decision support tools for showing constituents and the public the biological basis behind local
river management decisions. The graphs provide a solid base for supporting management decisions
because the relationships are based upon observations from several hundred sites, with multiple
observations often occurring for a given set of conditions. Fish-habitat relationships are especially
strong for species or taxa when graphs show one set of habitat conditions associated with peak fish
density, despite the wide range of habitat conditions in the state. The simple axes of the plots can be
readily used to plot conditions for a river site of interest, assess its suitability for various species of
fish, and compare and contrast it with other sites and rivers. The electronically available plots can be
simply cut and pasted into presentations.
This analysis provides useful benchmarks for assessing Michigan rivers for species because we
focused on relating species densities to limiting factors (e.g., temperature) and variables (i.e., LFY
and CA) well correlated with key aspects of habitat (i.e., temperature, depth, velocity, etc.) and
species distributions in Michigan (Bailey et al. 2004; Zorn et al. 2004). Thus, relationships between
5
these habitat factors and fish density can be used with site-based data to better identify what may be
limiting a population’s abundance at a site.
The relations we depict are analogous to traditional Habitat Suitability Index plots (e.g., Raleigh
et al. 1986) in that they show conditions where species do well, as indexed by fish density. However,
they differ in several respects including: response variable used (overall population density vs.
suitability for individual fish or life stage); measurement scale of response variable (actual densities
vs. 0-1 range of suitability scores); habitat variables chosen (a few key variables indexing local
conditions vs. many site-scale variables related to an individual fish’s use of microhabitats); and our
emphasis on describing central tendencies vs. site-scale limits to microhabitat use by individual life
stages. Since they are based on a statewide fish community dataset, our plots cover broad array
species and taxa, and are specific to Michigan.
Management Scenarios
Fish stocking represents a substantial investment of MDNR-FD’s resources, with hatchery-related
operations consuming roughly 30% of the agency’s budget (MDNR-FD, unpublished data). A good
portion of this expense is directed toward stocking streams with trout. These tools can be used to
support decisions related to stocking, such as whether or not to stock, and in some instances, what
minimum size limits to place on stocked waters. Rivers where temperature conditions are adequate
for trout survival (e.g., mean July temperatures consistently 68o F or less) and where there is no (or
very little) natural reproduction of trout should be considered for stocking. Obviously, streams with
temperature or low-flow yield conditions unsuited for trout should not be considered for stocking,
while those with marginal conditions would need to be investigated more closely. The three waters
shown in Figure 7 are among the most expensive stocking sites of non-migratory salmonids in
Michigan, and it behooves MDNR-FD to manage these fisheries to optimize its return on investment.
For example, lower minimum size limits seem appropriate in reaches such as the Muskegon River
below Croton dam, where thermal conditions might often limit annual survival. Higher size limits
seem more feasible when thermal constraints are reduced and fish can survive to larger (possibly
trophy) size. For example, the agency is currently experimenting with higher size limits for brown
trout and rainbow trout in a stocked reach of the Au Sable River below Mio Dam.
These statewide data will allow managers to readily assess, to some extent, the restoration or
rehabilitation potential of a site for various species of fish. These data could be used to assess thermal
impacts of Michigan’s 2500+ dams or major water discharges on downstream reaches. For example,
data characterizing LFY and CA conditions were used to characterize the Middle Branch River (a
tributary to the Muskegon River) downstream of a millpond in Marion (Figure 6). This information
suggested that the river at this location would likely be well suited to brown trout. Temperature
measurements upstream of the impoundment indicated likewise, but conditions below the
impoundment show substantial warming (Figure 6). It is likely that management efforts to create a
channel bypassing the impoundment will result in good conditions for trout in the river downstream
of the confluence of the bypass channel and the original river channel.
Development of fish passage at downstream dams on Great Lakes tributaries has the potential to
substantially increase population levels of migratory Great Lakes salmonids (e.g., Chinook salmon,
rainbow trout, coho salmon) and decrease the Michigan’s reliance on hatcheries for production of
these species. The plots (and associated data) could be used to provide general estimates of the
densities of these species in tributaries. Data relating maximum potential density to habitat variables
(e.g., Figure 7) may be especially appropriate for restoration work where thermal impacts are the
primary impediment.
Data relating fish densities to LFY also provide useful demonstrations as to the influence of lowflow water withdrawal on fishes. Reduced LFY values (and increased temperatures) associated with
6
water withdrawal would lower the potential of some streams for salmonids, especially in reaches that
presently provide thermally marginal conditions for trout reproduction and survival. For example,
reducing the LFY for the Iron River at the city of Iron River from its current value to 0.2 ft3*s-1*mi-2
would result in the stream becoming ill-suited for brook trout (Figure 4). Likewise, diminishing the
LFY of Middle Branch River at Marion from its current value to 0.05 ft3*s-1*mi-2 would in all
likelihood severely reduce its capacity to support self-sustaining brown trout populations (Figure 6).
Coldwater stream ecosystems may be most obviously affected by water withdrawal, but such effects
are likely not limited to them. Our study showed that for a given size of stream, densities of many
cool- and warm-water species declined with reductions in LFY (Figure 5; Appendix A). Such patterns
indicate these species may also be detrimentally affected by water withdrawal.
The products we developed are useful for comparing habitat use relationships among species. For
example, these data shed light on the usefulness of certain taxa (e.g., mottled sculpin) as indicators of
“coldwater” streams and their subsequent use in justifying trout stocking. Our data show lower
thermal tolerances of salmonids relative to mottled sculpin, suggesting that mottled sculpin presence
is not necessarily an indicator of a stream highly suited to salmonids (Figure 6). The data also
demonstrate distinct differences between mottled sculpin and slimy sculpin in thermal conditions
where each species is most abundant in Michigan (Appendix A). Similarly, our analyses suggest that
large populations of white suckers and low populations of trout may be more indicative of stream
temperature conditions marginal for trout (Figures 3 and 4) than competition with white sucker
(Moyle et al. 1983). Thus, these relationships support MDNR-FD’s current position to limit chemical
reclamations in marginal trout streams.
The spreadsheet model provides a simple tool with many potential uses. Managers having the
requisite physical data can use it to estimate the type of fish assemblage that might be expected at a
site. Such predictions might be useful when little or no fish survey data are available, and would
provide benchmarks for comparison with existing surveys. Managers can get some sense of how fish
assemblage structure changes upstream or downstream of a site by changing the CA value in the
model. At a larger scale, statewide stream classification and mapping efforts, such as Michigan’s
valley segment ecological classification (Seelbach et al. 1997), have used the model to predict fish
community structure in river segments throughout the state.
Managers can also use the spreadsheet model to explore how management actions that change
key habitat parameters (e.g., temperature) might influence the fish assemblage at a site. For example,
a next-generation version of the spreadsheet model described here has been developed to project fish
community responses to water withdrawal (i.e., LFY reductions and temperature increases), and was
used in support of groundwater protection legislation recently passed in Michigan (Zorn et al. 2008).
Limitations
The findings of this study and the utility of our results are limited in several ways. The surface
plots show where each species does well and where it might not do well (assuming equal historic
access). Despite the large number of sites included in this study, relatively few data (i.e., n < 10) were
available for certain combinations of LFY, CA, or July temperature conditions (Figure 2). Sometimes
this represented a lack of samples for a particular type of stream, while other times it resulted from a
lack of these types of streams in Michigan (e.g., streams with CA greater than 600 mi2 and LFY
values higher than 0.6 ft3*s-1*mi-2). Inadequate data could result in under-representation of the range
of suitable conditions for a species, and may lead to LFY-CA versus fish density plots for some
species with distinct peaks rather than a smooth surface with a single peak representing optimal
conditions (minor variation in peaks might also be attributed to how data were stratified for
summarization and plotted). Most pronounced examples of distinct peaks occurred for the set of
streams bound by CA values of 250 and 600 mi2 and LFY values of 0.05 and 0.10 ft3*s-1*mi-2. Three
7
of the six sites that met these criteria and had fish density data were on the Maple River. The Maple
River is a tributary to the Grand River that flows within an extremely low gradient, former glacial
drainageway (i.e., the valley is much larger than the present river), and supports large populations of
lake fishes. The low sample size and uniqueness of the Maple River resulted in discrete density peaks
for nine species, including black crappie, bluegill, bowfin, common carp, channel catfish, flathead
catfish, largemouth bass, pumpkinseed, tadpole madtom, and white crappie (Appendix A). In these
cases, broad patterns showing the general relation between habitat conditions and species density still
occurred frequently.
Errors or biases associated with data collection or model prediction could limit the accuracy of
the relationships we described. Ninety-percent exceedance flow values were often predicted and
temperature values were predicted when measurements were not available. Biases associated with
these modeling efforts were introduced into this analysis. However, we tried to minimize such errors
by excluding known problem sites, such as a set of very small (i.e., CA < 6 mi2) trout streams we
identified as having biased flow predictions (Zorn et al. 2002). Catchment area values, fish densities,
and nearly all temperatures were measured, so there is likely little error for these variables except
errors due to fish misidentification, equipment malfunction, or collection of temperature data not
representative of average conditions. Finally, our fish abundance data were limited to summer
collections, so resulting plots do not represent year-round densities for species that show strong
seasonal migrations or variation in density levels (e.g., Chinook salmon, coho salmon, and rainbow
trout).
The intent of developing simple, data-driven products limits the application of our results to basic
decision support uses. The strength of this study’s findings rests on the hundreds of surveys that went
into building the relations we portrayed. Our wedge-shaped scatterplots of trout density versus
temperature (Figures 7 and 8) show when temperature limits fish abundance, but do not identify other
factors limiting fish density beyond the thermal constraints of the stream (Terrell et al. 1996;
Thompson et al. 1996). With the fish density surface plots, we attempted to show dominant relations
between species density and habitat conditions by averaging measured values across groups of similar
sites. Our results do not show the amount of variation in conditions and fish densities that occurs
within each group of sites. Habitat and fish density values were simply averaged and plotted for each
subset of sites meeting particular habitat criteria. Comparison between the range of individual site
conditions (Figure 2) and range of the average values plotted (e.g., Figures 4 and 5) show this. As a
result, conditions of some sites (e.g., the Manistee River in Figure 5) now appear to lie off the surface
of the graph. In such cases, it is usually appropriate to extrapolate the observed fish density trend
beyond the surface of the plot to the conditions of the site. Though multivariate modeling approaches
would certainly have explained more variation in species abundances, we limited our summaries to
two dimensional plots to make them more user-friendly. In a similar fashion, the spreadsheet model
for characterizing suitability of sites for species included three variables, but could be further refined
by adding additional variables (e.g., Great Lakes accessibility). Despite these shortcomings, our
experience with MDNR-FD managers and the public indicate that these simple, data-driven, decision
support tools will prove quite useful.
We believe the approach of using LFY, CA, and July temperature as axes for contrasting streams
and displaying fish abundance patterns is widely applicable. The relationships we describe are most
applicable to Michigan and may also apply to adjacent glaciated regions. This seems especially true
for relationships between fish abundance and July temperature, which are more directly tied to fish
bioenergetics than those for LFY and CA (Zorn et al. 2002). Thus, our LFY-CA based plots may have
limited applicability to other regions due to differences in relationships fish density and key factors
influencing it (e.g., climate, latitude, altitude, watershed geology, etc.). Still, we think our approach
could be used to develop models for other regions that relate fish density to key habitat variables. In
addition, the Michigan plots can serve as initial models for comparison with fish-habitat relations
developed in other regions.
8
Acknowledgments
Numerous crews from the Michigan Department of Natural Resources (MDNR), United States
Department of Agriculture Forest Service, the University of Michigan, and Michigan State University
collected the actual field data. Many former University of Michigan students contributed data to this
study, namely Matthew Baker, John Fay, Dana Infante, Catherine Riseng, and Kevin Wehrly. James
Gapczynski performed initial entry of fisheries data in MRI databases. James Schneider, Philip
Schneeberger, and Chris Freiburger provided thoughtful advice and manuscript reviews. We
acknowledge MDNR Institute for Fisheries Research’s Richard Clark and Paul Seelbach for
administrative support, and Ellen Johnston and Alan Sutton for assistance in preparation of this
manuscript. This research was supported with funds from the Federal Aid in Sport Fish Restoration
Act (Study 230680, Project F-81-R, Michigan), MDNR Fisheries Division, and the Michigan
Professional Employees Society.
9
0
30
60
120
Miles
Figure 1.–Sites on Michigan streams with fish density data for salmonids (80 sites as open circles)
or the entire fish assemblage (298 sites in black).
10
10
A
Low-flow yield (cfs/mi2)
1
0.1
0.01
0.001
0.0001
0.1
1
10
100
1000
10000
Catchment area (mi)
July weekly temperature range (F)
35
B
30
25
20
15
10
5
0
45
50
55
60
65
July mean temperature (F)
70
75
80
Figure 2.–Data summary grids used to summarize species density and habitat conditions at Michigan
Rivers Inventory sites along axes of low-flow yield and catchment area (A) and mean and weekly range
in July temperature (B). Symbols distinguish between sites where fish were sampled by mark-recapture
(gray circles) and rotenone or multi-pass depletion (black circles) methods.
11
July mean temperature (F)
90% Exceedence flow yield (cfs/mi2)
2
78
1
76
74
72
0.1
70
68
66
64
0.01
10
100
1000
4000
62
Catchment Area (mi )
2
Figure 3.–Relationship between July mean temperature, catchment area, and low-flow yield for
Michigan rivers.
12
90% Exceedence flow yield (cfs/mi2)
90% Exceedence flow yield (cfs/mi2)
Brook Trout (lb/ac)
2
25
1
20
15
0.1
10
5
0
0.01
10
100
1000
White sucker (lb/ac)
2
60
1
50
40
30
0.1
20
10
0
0.01
10
4000
Rock bass (lb/ac)
2
90% Exceedence flow yield (cfs/mi2)
90% Exceedence flow yield (cfs/mi2)
Catchment Area (mi2)
18
16
14
12
10
8
6
4
2
0
1
0.1
0.01
10
100
1000
Catchment Area (mi2)
4000
Smallmouth bass (lb/ac)
2
12
1
10
8
6
0.1
4
2
0
0.01
10
4000
100
1000
Catchment Area (mi2)
100
1000
Catchment Area (mi2)
4000
Figure 4.–Average density of brook trout, white sucker, rock bass, and smallmouth bass in Michigan
streams versus low-flow yield and catchment area. Conditions are shown on each plot for the Iron River
at Iron River (circle), the Flat River at Belding (square), and the Raisin River at Monroe (triangle). Note
that density scales differ among graphs.
13
90% Exceedence flow yield (cfs/mi2)
90% Exceedence flow yield (cfs/mi2)
All Salmonids (lb/ac)
2
80
1
70
60
50
40
0.1
30
20
10
0
0.01
10
100
1000
Dace (lb/ac)
2
9
8
7
6
5
4
3
2
1
0
1
0.1
0.01
10
4000
Suckers (lb/ac)
2
90% Exceedence flow yield (cfs/mi2)
90% Exceedence flow yield (cfs/mi2)
Catchment Area (mi2)
250
1
200
150
0.1
100
50
0
0.01
10
100
1000
Catchment Area (mi2)
4000
100
1000
Catchment Area (mi2)
4000
Catfishes (lb/ac)
2
45
40
35
30
25
20
15
10
5
0
1
0.1
0.01
10
100
1000
Catchment Area (mi2)
4000
Figure 5.–Average density of trout, dace, suckers, and catfishes in Michigan streams versus lowflow yield and catchment area. Conditions are shown on each plot for the Manistee River at Grayling
(circle), the Maple River (a Grand River tributary) at Maple Rapids (triangle), and the Manistique River
at Manistique (square). Note that density scales differ among graphs.
14
1
70
60
50
40
0.1
30
20
10
0
0.01
10
100
1000
Est. July weekly temperature range (F)
90% Exceedence flow yield (cfs/mi2)
Brown Trout (lb/ac)
2
8
10
1
0
5
55
500
450
400
350
300
250
200
150
100
50
0
15
10
5
July weekly temperature range (F)
July weekly temperature range (F)
3
2
60
65
70
75
80
Est. July weekly mean temperature (F)
20
60
65
70
75
July mean temperature (F)
5
4
15
4000
Brown trout (#/ac)
55
7
6
20
Catchment Area (mi2)
25
Mottled sculpin (lb/ac)
25
80
Creek chub (lb/ac)
25
25
20
20
15
15
10
5
10
0
5
55
60
65
70
75
July mean temperature (F)
80
Figure 6.–Relationships of biomass density of brown trout, creek chub, and mottled sculpin and
numerical density of brown trout to low-flow yield, catchment area, and July mean temperature.
Conditions of the Middle Branch River upstream (circle) and downstream (triangle) of Marion Millpond
are shown on temperature plots. July weekly temperature range for Middle Branch River is estimated
from the monthly temperature range. Note that density scales differ among graphs.
15
4000
3500
Maximum potential
brown trout density
Unstocked
Stocked
Brown trout (#/acre)
3000
2500
2000
1500
1000
500
0
55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
July mean temperature (F)
Figure 7.–Numerical density of brown trout in unstocked (squares) and stocked (triangles) Michigan
streams versus July mean temperature (n = 152). The line fitted along the upper portion of the data
represents a hypothesized relationship between July mean temperature and the maximum potential
brown trout density for Michigan rivers. Zero density values are not shown. Horizontal lines show
range in mean July temperature from 1998 to 2001 for the Au Sable River below Mio dam (longest
line), the Manistee River below Hodenpyle dam (medium length line), and the Muskegon River below
Croton dam (shortest line). Data for an additional 29 unstocked sites were obtained from Michigan
Department of Natural Resources Fisheries Division Status and Trends surveys (T. Wills, unpublished
data).
16
2500
Brook trout (#/acre)
2000
1500
1000
500
0
55
57
59
61
63
65
67
69
71
73
75
July mean temperature (F)
Figure 8.–Numerical density of brook trout in Michigan streams versus July mean temperature (n
= 139). Zero density values are not shown. Data for 29 unstocked sites were obtained from Michigan
Department of Natural Resources Fisheries Division Status and Trends surveys (T. Wills, unpublished
data).
17
78
July mean temperature (F)
76
Smallmouth
bass
74
Quillback
Freshwater drum
Walleye
Shorthead
redhorse
Rock bass
72
Pirate perch
White sucker
Stoneroller
68
Creek chub
66
Log perch
Tadpole madtom
Common shiner
Grass pickerel
70
64
Channel catfish
Burbot
Longnose dace
Johnny darter
Northern redbelly dace
Mottled sculpin
Brook stickleback
Rainbow trout
Chinook salmon
Brown trout
62
Brook trout
60
Coho salmon
Slimy sculpin
58
00
40 0
0
30
00
20
00
100
80
0
60
0
40
0
0
10
80
20
60
40
20
10
Catchment area (mi2)
Figure 9.–July mean temperature and catchment area values of “optimal” sites for 68 common
fishes in Michigan rivers. Common names of select fishes are shown in the vicinity of their optimal
values. Optimal values occur in Table 2.
18
Table 1.–Species and taxonomic groups used in surface plots of fish density versus habitat.
Numbers by species names indicate membership in numbered groups (in bold type). Densities of less
common species (not listed) were included with their corresponding taxonomic group.
Group Species or group name
1
1
1
1
1
1
1
1
2
2
3
3
3
3
4
5
5
5
6
6
6
6
6
6,7
6,7
6,7
6,7
6,7
8
8
8
8
8
8
8
Group Species or group name
Shiners (1)
Spotfin shiner Cyprinella spiloptera
Common shiner Luxilus cornutus
Striped shiner Luxilus chrysocephalus
Redfin shiner Lythrurus umbratilis
Golden shiner Notemigonus crysoleucas
Rosyface shiner Notropis rubellus
Sand shiner Notropis stramineus
Mimic shiner Notropis volucellus
Minnows (2)
Bluntnose minnow Pimephales notatus
Fathead minnow Pimephales promelas
Chubs and stoneroller (3)
Central stoneroller Campostoma anomalum
Creek chub Semotilus atromaculatus
Hornyhead chub Nocomis biguttatus
River chub Nocomis micropogon
Carp and goldfish (4)
Common carp Cyprinus carpio
Dace (5)
Blacknose dace Rhinichthys atratulus
Longnose dace Rhinichthys cataractae
Northern redbelly dace Phoxinus eos
Suckers (6) and Redhorses (7)
Quillback Carpiodes cyprinus
White sucker Catostomus commersonii
Lake chubsucker Erimyzon sucetta
Northern hog sucker Hypentelium nigricans
Spotted sucker Minytrema melanops
Silver redhorse Moxostoma anisurum
Black redhorse Moxostoma duquesnei
Golden redhorse Moxostoma erythrurum
Shorthead redhorse Moxostoma macrolepidotum
Greater redhorse Moxostoma valenciennesi
Catfishes (8)
Black bullhead Ameiurus melas
Brown bullhead Ameiurus nebulosus
Yellow bullhead Ameiurus natalis
Channel catfish Ictalurus punctatus
Stonecat Noturus flavus
Tadpole madtom Noturus gyrinus
Flathead catfish Pylodictis olivaris
19
9
9
10
10
10
10
10
11
11
12
12
12
12
12
12
12
12
12
13
13
13,14
13,14
13,14
13,14
13,14
Pikes (9)
Grass pickerel Esox americanus
Northern pike Esox lucius
Salmonids (10)
Brook trout Salvelinus fontinalis
Brown trout Salmo trutta
Coho salmon Oncorhynchus kisutch
Rainbow trout Oncorhynchus mykiss
Chinook salmon Oncorhynchus tshawytscha
Sculpins (11)
Mottled sculpin Cottus bairdi
Slimy sculpin Cottus cognatus
Sunfishes (12)
Rock bass Ambloplites rupestris
Green sunfish Lepomis cyanellus
Bluegill Lepomis macrochirus
Longear sunfish Lepomis megalotis
Pumpkinseed Lepomis gibbosus
Smallmouth bass Micropterus dolomieu
Largemouth bass Micropterus salmoides
White crappie Pomoxis annularis
Black crappie Pomoxis nigromaculatus
Perches (13) and Darters (14)
Walleye Sander vitreus
Yellow perch Perca flavescens
Logperch Percina caprodes
Blackside darter Percina maculata
Greenside darter Etheostoma blennioides
Rainbow darter Etheostoma caeruleum
Johnny darter Etheostoma nigrum
Species not pooled
Bowfin Amia calva
Gizzard shad Dorosoma cepedianum
Central mudminnow Umbra limi
Pirate perch Aphredoderus sayanus
Burbot Lota lota
Brook silverside Labidesthes sicculus
Brook stickleback Culaea inconstans
Hybrid sunfish
Freshwater drum Aplodinotus grunniens
Table 2.–Projected suitability of the Huron River at Delhi Road for common Michigan fishes based upon comparisons with “optimal” July
mean temperature, catchment area, and low-flow yield conditions for each species. Catchment area is 690 mi2. July mean temperature was
72.6°F and low-flow yield was 0.278 cfs/mi2. Species list is sorted based upon mean and minimum composite suitability scores. Species
“optimal” data are shown for reference.
Species
20
Silver redhorse
Spotted sucker
Walleye
Carp
Log perch
Northern hog sucker
Rosyface shiner
Sand shiner
Shorthead redhorse
Striped shiner
Black crappie
Bowfin
Gizzard shad
Largemouth Bass
Smallmouth bass
Stonecat
Brown bullhead
Freshwater drum
Yellow perch
Black redhorse
Golden redhorse
Mimic shiner
Bluegill
Bluntnose minnow
Brook silverside
Channel catfish
No. of
Composite score
optimal sites Min
Mean
15
15
19
40
24
33
24
13
15
13
24
14
6
28
42
29
8
4
31
12
34
11
31
29
8
27
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
2
2
2
3
3
3
2
2
2
2
4.0
4.0
4.0
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.3
3.3
3.3
3.3
3.3
3.3
3.3
3.3
3.3
3.0
3.0
3.0
3.0
3.0
3.0
3.0
Score by variable (4=Hi)
JulyMn
CA
LFY
4
4
4
4
4
4
4
4
4
4
3
4
4
4
4
4
4
4
4
3
3
3
4
4
4
3
4
4
4
4
3
4
3
3
3
3
4
3
3
3
3
3
2
4
2
3
3
3
2
2
3
4
4
4
4
3
4
3
4
4
4
4
3
3
3
3
3
3
4
2
4
3
3
3
3
3
2
2
Species optima data
July mean
Log10 catchment Log10 low-flow
area (mi2)
yield (cfs/mi2)
temp. (°F)
Mean S. Dev
Mean S. Dev
Mean S. Dev
73.9
73.7
72.9
74.0
71.7
73.7
72.3
72.0
73.2
73.3
74.6
72.3
73.3
72.8
73.9
73.3
73.3
73.8
71.1
73.9
74.2
74.6
71.8
71.3
74.4
75.1
4.6
3.3
2.7
3.6
4.1
3.5
3.1
3.8
3.0
3.7
3.0
4.5
3.6
3.6
2.8
3.3
4.7
3.8
4.9
2.5
3.0
3.4
4.3
5.4
4.1
3.0
2.72
2.60
2.85
2.59
2.33
2.67
2.51
2.56
2.65
2.42
2.81
2.24
3.04
2.12
2.55
2.51
1.75
3.05
2.21
2.56
2.61
2.36
1.96
2.06
2.42
2.91
0.34
0.51
0.48
0.54
0.56
0.43
0.34
0.39
0.35
0.52
0.58
0.62
0.39
0.80
0.50
0.37
0.98
0.46
0.59
0.37
0.33
0.54
0.70
0.66
0.49
0.43
-0.73
-0.73
-0.68
-1.00
-0.66
-0.77
-0.60
-0.70
-0.65
-0.66
-0.79
-0.91
-0.97
-0.85
-0.74
-0.89
-0.75
-1.13
-0.78
-0.71
-0.95
-0.95
-0.89
-1.23
-0.72
-0.82
0.37
0.39
0.43
0.51
0.32
0.29
0.39
0.37
0.34
0.46
0.41
0.62
0.45
0.54
0.30
0.35
0.45
0.43
0.58
0.22
0.41
0.55
0.55
0.69
0.16
0.24
Table 2.–Continued.
Species
21
Flathead catfish
Northern pike
Rock bass
Spotfin shiner
Yellow bullhead
River chub
Greenside darter
Longnose dace
Quillback
Rainbow darter
Burbot
Common shiner
Greater redhorse
Longear sunfish
Pumpkinseed
Hornyhead chub
White crappie
Blackside darter
Tadpole madtom
White sucker
Grass pickerel
Lake chubsucker
Golden shiner
Green sunfish
Chinook salmon
Blacknose dace
Fathead minnow
Mottled sculpin
Mudminnow
No. of
Composite score
optimal sites Min
Mean
10
33
43
18
37
14
20
18
10
22
19
42
18
11
23
33
7
44
15
39
26
4
7
32
8
32
10
32
10
2
2
2
2
2
1
2
2
2
2
1
1
1
1
1
0
0
1
1
1
0
0
0
0
1
0
0
0
0
3.0
3.0
3.0
3.0
3.0
3.0
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.3
2.3
2.3
2.3
2.3
2.0
2.0
1.7
1.7
1.7
1.7
1.7
Score by variable (4=Hi)
JulyMn
CA
LFY
2
4
4
3
4
4
4
2
2
3
3
3
4
4
4
4
4
3
4
3
3
3
3
3
1
1
3
1
2
4
2
2
4
2
1
2
2
3
2
1
1
3
1
1
0
4
1
1
1
0
0
0
0
2
0
0
0
0
3
3
3
2
3
4
2
4
3
3
4
4
1
3
3
4
0
3
2
3
4
4
3
3
2
4
2
4
3
July mean
temp. (°F)
Mean S. Dev
75.4
71.3
72.6
74.6
73.0
72.9
71.9
67.5
75.6
70.2
69.6
70.6
73.8
72.9
72.3
70.6
73.8
70.2
71.9
69.1
70.2
68.0
68.7
70.4
63.0
66.7
69.4
64.1
68.1
2.1
3.9
3.0
2.6
3.7
2.3
3.1
4.4
2.9
3.6
3.6
3.4
3.2
3.4
4.1
4.2
3.4
3.3
4.7
4.3
3.7
6.4
4.5
4.2
5.7
3.4
4.4
5.4
3.1
Species optima data
Log10 catchment Log10 low-flow
area (mi2)
yield (cfs/mi2)
Mean S. Dev
Mean S. Dev
3.11
2.22
2.22
2.71
2.11
2.39
2.31
1.90
3.26
1.84
2.15
1.79
2.50
1.85
1.82
1.76
2.61
2.03
2.05
1.80
1.67
1.46
1.54
1.63
1.65
1.38
1.56
1.33
1.33
0.55
0.51
0.51
0.33
0.65
0.24
0.45
0.65
0.46
0.71
0.44
0.62
0.40
0.54
0.54
0.52
0.49
0.48
0.45
0.57
0.49
0.34
0.50
0.56
0.92
0.45
0.58
0.51
0.32
-0.81
-1.06
-0.85
-0.81
-0.95
-0.45
-0.98
-0.57
-0.84
-0.87
-0.55
-0.84
-1.07
-1.02
-1.04
-0.74
-1.17
-0.93
-1.37
-0.96
-0.69
-0.50
-1.35
-1.06
-0.08
-0.85
-1.22
-0.56
-1.27
0.31
0.63
0.55
0.23
0.67
0.26
0.38
0.25
0.38
0.59
0.34
0.60
0.31
0.87
0.66
0.60
0.23
0.69
0.63
0.51
0.56
0.15
0.94
0.71
0.40
0.61
0.47
0.39
0.87
Table 2.–Continued.
Species
22
Black bullhead
Brook trout
Brown trout
Coho salmon
Johnny darter
Pirate perch
Rainbow trout
Redfin shiner
Slimy sculpin
Stoneroller
Brook stickleback
Creek chub
Northern redbelly dace
No. of
Composite score
optimal sites Min
Mean
18
38
52
7
20
6
31
3
17
10
14
29
9
0
0
0
0
0
0
0
0
0
0
0
0
0
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.0
1.0
1.0
Score by variable (4=Hi)
JulyMn
CA
LFY
2
0
0
1
2
4
0
4
0
2
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
4
4
3
2
0
4
0
4
2
3
2
3
July mean
temp. (°F)
Mean S. Dev
68.8
60.8
62.1
61.5
67.8
71.2
63.1
69.9
59.7
69.0
63.3
66.9
65.9
3.4
4.3
3.5
5.9
3.7
4.1
3.9
5.4
5.3
2.8
3.3
3.1
2.7
Species optima data
Log10 catchment Log10 low-flow
area (mi2)
yield (cfs/mi2)
Mean S. Dev
Mean S. Dev
1.68
0.89
1.40
1.71
1.37
1.59
1.45
1.53
1.20
1.34
1.11
1.23
1.40
0.51
0.60
0.62
0.44
0.44
0.45
0.65
0.56
0.70
0.57
0.34
0.36
0.45
-1.24
-0.42
-0.35
-0.18
-1.46
-2.44
-0.38
-1.86
-0.58
-1.48
-1.16
-1.33
-0.91
0.63
0.40
0.44
0.38
0.72
0.24
0.44
0.25
0.46
0.77
0.64
0.71
0.59
Table 3.–Projected suitability of the Hunt Creek at East Fish Lake Road for common Michigan fishes based upon comparisons with
“optimal” July mean temperature, catchment area, and low-flow yield conditions for each species. Catchment area is 5 mi2. July mean
temperature was 58.5°F and low-flow yield was 0.806 cfs/mi2. Species list is sorted based upon mean and minimum composite suitability
scores. Species “optimal” data are shown for reference.
Species
23
Brook trout
Chinook salmon
Slimy sculpin
Brown trout
Rainbow trout
Coho salmon
Mottled sculpin
Brook stickleback
Brown bullhead
Blacknose dace
Bluegill
Common shiner
Creek chub
Golden shiner
Grass pickerel
Green sunfish
Largemouth Bass
Mudminnow
Northern redbelly dace
Rainbow darter
Stoneroller
Black bullhead
Blackside darter
Bowfin
Burbot
Fathead minnow
No. of
Composite score
optimal sites Min
Mean
38
8
17
52
31
7
32
14
8
32
31
42
29
7
26
32
28
10
9
22
10
18
44
14
19
10
3
2
2
2
2
0
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3.3
3.0
3.0
2.3
2.3
2.3
2.0
1.7
1.3
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
0.7
0.7
0.7
0.7
0.7
Score by variable (4=Hi)
JulyMn
CA
LFY
3
3
4
2
2
3
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
2
3
2
2
0
2
2
2
1
1
1
2
1
1
1
1
1
1
1
2
1
0
0
0
2
3
4
2
3
3
4
2
1
2
2
2
2
1
2
2
2
2
2
2
2
1
1
2
2
2
0
Species optima data
July mean
Log10 catchment Log10 low-flow
area (mi2)
yield (cfs/mi2)
temp. (°F)
Mean S. Dev
Mean S. Dev
Mean S. Dev
60.8
63.0
59.7
62.1
63.1
61.5
64.1
63.3
73.3
66.7
71.8
70.6
66.9
68.7
70.2
70.4
72.8
68.1
65.9
70.2
69.0
68.8
70.2
72.3
69.6
69.4
4.3
5.7
5.3
3.5
3.9
5.9
5.4
3.3
4.7
3.4
4.3
3.4
3.1
4.5
3.7
4.2
3.6
3.1
2.7
3.6
2.8
3.4
3.3
4.5
3.6
4.4
0.89
1.65
1.20
1.40
1.45
1.71
1.33
1.11
1.75
1.38
1.96
1.79
1.23
1.54
1.67
1.63
2.12
1.33
1.40
1.84
1.34
1.68
2.03
2.24
2.15
1.56
0.60
0.92
0.70
0.62
0.65
0.44
0.51
0.34
0.98
0.45
0.70
0.62
0.36
0.50
0.49
0.56
0.80
0.32
0.45
0.71
0.57
0.51
0.48
0.62
0.44
0.58
-0.42
-0.08
-0.58
-0.35
-0.38
-0.18
-0.56
-1.16
-0.75
-0.85
-0.89
-0.84
-1.33
-1.35
-0.69
-1.06
-0.85
-1.27
-0.91
-0.87
-1.48
-1.24
-0.93
-0.91
-0.55
-1.22
0.40
0.40
0.46
0.44
0.44
0.38
0.39
0.64
0.45
0.61
0.55
0.60
0.71
0.94
0.56
0.71
0.54
0.87
0.59
0.59
0.77
0.63
0.69
0.62
0.34
0.47
Table 3.–Continued.
Species
24
Hornyhead chub
Johnny darter
Lake chubsucker
Longear sunfish
Longnose dace
Pumpkinseed
Redfin shiner
River chub
Rock bass
Rosyface shiner
Striped shiner
Walleye
White sucker
Yellow bullhead
Yellow perch
Black crappie
Bluntnose minnow
Carp
Gizzard shad
Log perch
Mimic shiner
Northern pike
Quillback
Sand shiner
Shorthead redhorse
Silver redhorse
Spotted sucker
Black redhorse
Brook silverside
No. of
Composite score
optimal sites Min
Mean
33
20
4
11
18
23
3
14
43
24
13
19
39
37
31
24
29
40
6
24
11
33
10
13
15
15
15
12
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.0
0.0
Score by variable (4=Hi)
JulyMn
CA
LFY
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
2
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
2
1
2
0
2
2
2
2
2
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1
0
0
July mean
temp. (°F)
Mean S. Dev
70.6
67.8
68.0
72.9
67.5
72.3
69.9
72.9
72.6
72.3
73.3
72.9
69.1
73.0
71.1
74.6
71.3
74.0
73.3
71.7
74.6
71.3
75.6
72.0
73.2
73.9
73.7
73.9
74.4
4.2
3.7
6.4
3.4
4.4
4.1
5.4
2.3
3.0
3.1
3.7
2.7
4.3
3.7
4.9
3.0
5.4
3.6
3.6
4.1
3.4
3.9
2.9
3.8
3.0
4.6
3.3
2.5
4.1
Species optima data
Log10 catchment Log10 low-flow
area (mi2)
yield (cfs/mi2)
Mean S. Dev
Mean S. Dev
1.76
1.37
1.46
1.85
1.90
1.82
1.53
2.39
2.22
2.51
2.42
2.85
1.80
2.11
2.21
2.81
2.06
2.59
3.04
2.33
2.36
2.22
3.26
2.56
2.65
2.72
2.60
2.56
2.42
0.52
0.44
0.34
0.54
0.65
0.54
0.56
0.24
0.51
0.34
0.52
0.48
0.57
0.65
0.59
0.58
0.66
0.54
0.39
0.56
0.54
0.51
0.46
0.39
0.35
0.34
0.51
0.37
0.49
-0.74
-1.46
-0.50
-1.02
-0.57
-1.04
-1.86
-0.45
-0.85
-0.60
-0.66
-0.68
-0.96
-0.95
-0.78
-0.79
-1.23
-1.00
-0.97
-0.66
-0.95
-1.06
-0.84
-0.70
-0.65
-0.73
-0.73
-0.71
-0.72
0.60
0.72
0.15
0.87
0.25
0.66
0.25
0.26
0.55
0.39
0.46
0.43
0.51
0.67
0.58
0.41
0.69
0.51
0.45
0.32
0.55
0.63
0.38
0.37
0.34
0.37
0.39
0.22
0.16
Table 3.–Continued.
Species
25
Channel catfish
Flathead catfish
Freshwater drum
Golden redhorse
Greater redhorse
Greenside darter
Northern hog sucker
Pirate perch
Smallmouth bass
Spotfin shiner
Stonecat
Tadpole madtom
White crappie
No. of
Composite score
optimal sites Min
Mean
27
10
4
34
18
20
33
6
42
18
29
15
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Score by variable (4=Hi)
JulyMn
CA
LFY
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
July mean
temp. (°F)
Mean S. Dev
75.1
75.4
73.8
74.2
73.8
71.9
73.7
71.2
73.9
74.6
73.3
71.9
73.8
3.0
2.1
3.8
3.0
3.2
3.1
3.5
4.1
2.8
2.6
3.3
4.7
3.4
Species optima data
Log10 catchment Log10 low-flow
area (mi2)
yield (cfs/mi2)
Mean S. Dev
Mean S. Dev
2.91
3.11
3.05
2.61
2.50
2.31
2.67
1.59
2.55
2.71
2.51
2.05
2.61
0.43
0.55
0.46
0.33
0.40
0.45
0.43
0.45
0.50
0.33
0.37
0.45
0.49
-0.82
-0.81
-1.13
-0.95
-1.07
-0.98
-0.77
-2.44
-0.74
-0.81
-0.89
-1.37
-1.17
0.24
0.31
0.43
0.41
0.31
0.38
0.29
0.24
0.30
0.23
0.35
0.63
0.23
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Approved by Tammy J. Newcomb
27
28
Appendix A
Relationships between low-flow yield, catchment area, July temperature attributes, and fish biomass
density at the assemblage, taxonomic group, and species levels. Relationships between July temperature
attributes and numerical density are shown for brown trout, brook trout, and rainbow trout.
29
Appendix A Index
Golden shiner.................................................. 38
Grass pickerel.................................................. 48
Greater redhorse.............................................. 46
Greenside darter.............................................. 56
Green sunfish.................................................. 52
Hornyhead chub.............................................. 41
Hybrid sunfish................................................. 59
Johnny darter................................................... 56
Lake chubsucker............................................. 43
Largemouth bass............................................. 53
Logperch......................................................... 55
Longear sunfish............................................... 52
Longnose dace................................................ 42
Mimic shiner................................................... 39
Mottled sculpin............................................... 51
Northern hog sucker........................................ 44
Northern pike.................................................. 49
Northern redbelly dace.................................... 42
Pirate perch..................................................... 58
Pumpkinseed................................................... 53
Quillback......................................................... 43
Rainbow darter................................................ 56
Rainbow trout.................................................. 50
Redfin shiner................................................... 38
River chub....................................................... 41
Rock bass........................................................ 51
Rosyface shiner............................................... 38
Sand shiner...................................................... 39
Shorthead redhorse.......................................... 45
Silver redhorse................................................ 44
Slimy sculpin.................................................. 51
Smallmouth bass............................................. 53
Spotfin shiner.................................................. 37
Spotted sucker................................................. 44
Stonecat........................................................... 47
Striped shiner.................................................. 37
Tadpole madtom............................................. 48
Walleye........................................................... 54
White crappie.................................................. 54
White sucker................................................... 43
Yellow bullhead.............................................. 47
Yellow perch................................................... 55
4. Numerical density (#/acre) vs. temperature.... 60
Brook trout...................................................... 60
Brown trout..................................................... 60
Rainbow trout.................................................. 60
1. Assemblage level............................................ 31
All Species (lb/acre)........................................ 31
Species Richness............................................. 31
2. Taxonomic groupings (lb/acre)....................... 32
All Cyprinids................................................... 32
Carp and Goldfish........................................... 33
Catfishes.......................................................... 34
Chubs and Central Stoneroller........................ 33
Dace................................................................ 33
Darters............................................................. 36
Minnows......................................................... 32
Perches............................................................ 36
Pikes................................................................ 35
Redhorses........................................................ 34
Salmonids........................................................ 35
Sculpins........................................................... 35
Shiners............................................................. 32
Suckers............................................................ 34
Sunfishes......................................................... 36
3. Species Level.................................................. 37
Black bullhead................................................ 46
Black crappie.................................................. 54
Blacknose dace................................................ 42
Black redhorse................................................ 45
Blackside darter.............................................. 55
Bluegill............................................................ 52
Bluntnose minnow.......................................... 39
Bowfin............................................................. 57
Brook silverside.............................................. 58
Brook stickleback............................................ 59
Brook trout...................................................... 49
Brown bullhead............................................... 46
Brown trout..................................................... 49
Burbot............................................................. 58
Central mudminnow........................................ 57
Central stoneroller........................................... 40
Channel catfish................................................ 47
Chinook salmon.............................................. 50
Coho salmon................................................... 50
Common carp.................................................. 41
Common shiner............................................... 37
Creek chub...................................................... 40
Fathead minnow.............................................. 40
Flathead catfish............................................... 48
Freshwater drum............................................. 59
Gizzard shad.................................................... 57
Golden redhorse.............................................. 45
30
1. Assemblage level
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Species Richness
35
30
25
20
0.1
15
10
5
0.01
10
100
1000
25
30
20
25
20
15
15
10
10
5
5
4000
55
2
Catchment area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
All Species (lb/acre)
600
500
400
300
0.1
200
100
0
0.01
10
100
1000
4000
25
350
20
300
250
15
200
150
10
100
50
5
55
2
Catchment Area (mi )
31
60
65
70
75
July mean temperature (F)
80
2. Taxonomic groupings (lb/acre)
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
All Cyprinids
400
350
300
250
200
0.1
150
100
50
0
0.01
10
100
1000
25
160
140
20
120
100
15
80
60
40
10
20
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Shiners
12
10
8
6
0.1
4
2
0
0.01
10
100
1000
25
9
8
20
7
6
5
15
4
3
2
10
1
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Minnows
7
6
5
4
3
0.1
2
1
0
0.01
10
100
1000
25
3.5
3
20
2.5
2
15
1.5
1
10
0.5
0
5
55
4000
2
Catchment Area (mi )
32
60
65
70
75
July mean temperature (F)
80
2
1
70
60
50
40
30
0.1
20
10
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Chubs and Central Stoneroller
25
30
20
25
20
15
15
10
10
5
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
250
200
150
0.1
100
50
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Carp and Goldfish
25
160
140
20
120
100
15
80
60
40
10
20
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
9
8
7
6
5
4
3
0.1
2
1
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Dace
25
10
9
8
7
6
5
4
3
2
1
0
20
15
10
5
55
4000
2
Catchment Area (mi )
33
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Suckers
250
200
150
0.1
100
50
0
0.01
10
100
1000
25
120
20
100
80
15
60
40
10
20
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Redhorses
200
180
160
140
120
100
80
60
40
20
0
0.1
0.01
10
100
1000
25
100
90
80
70
60
50
40
30
20
10
0
20
15
10
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Catfishes
2
30
25
20
15
0.1
10
5
0
0.01
10
100
1000
25
25
20
20
15
15
10
5
10
0
5
55
4000
2
Catchment Area (mi )
34
60
65
70
75
July mean temperature (F)
80
2
1
14
12
10
8
6
0.1
4
2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Pikes
25
7
6
20
5
4
15
3
2
10
1
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
40
35
30
25
20
0.1
15
10
5
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Salmonids
25
50
45
40
35
30
25
20
15
10
5
0
20
15
10
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
14
12
10
8
6
0.1
4
2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Sculpins
2
25
8
7
20
6
5
15
4
3
2
10
1
0
5
55
4000
2
Catchment Area (mi )
35
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Sunfishes
30
25
20
15
0.1
10
5
0
0.01
10
100
1000
25
30
20
25
20
15
15
10
10
5
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Perches
9
8
7
6
5
4
3
0.1
2
1
0
0.01
10
100
1000
25
4.5
4
20
3.5
3
2.5
15
2
1.5
1
10
0.5
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Darters
2
4
3.5
3
2.5
2
0.1
1.5
1
0.5
0
0.01
10
100
1000
25
3
20
2.5
2
15
1.5
1
10
0.5
0
5
55
4000
2
Catchment Area (mi )
36
60
65
70
75
July mean temperature (F)
80
Spotfin shiner
2
1
3
2.5
2
1.5
0.1
1
0.5
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
3. Species Level
25
1.2
20
1
0.8
15
0.6
0.4
10
0.2
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
12
10
8
6
0.1
4
2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Common shiner
25
8
7
20
6
5
15
4
3
2
10
1
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0.1
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Striped shiner
2
25
1.8
1.6
20
1.4
1.2
1
15
0.8
0.6
0.4
10
0.2
0
5
55
4000
2
Catchment Area (mi )
37
60
65
70
75
July mean temperature (F)
80
2
1
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0.1
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Redfin shiner
25
0.018
0.016
20
0.014
0.012
0.01
15
0.008
0.006
0.004
10
0.002
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Golden shiner
0.7
0.6
0.5
0.4
0.3
0.1
0.2
0.1
0
0.01
10
100
1000
25
0.45
0.4
20
0.35
0.3
0.25
15
0.2
0.15
0.1
10
0.05
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Rosyface shiner
2
0.8
0.7
0.6
0.5
0.4
0.1
0.3
0.2
0.1
0
0.01
10
100
1000
25
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
20
15
10
5
55
4000
2
Catchment Area (mi )
38
60
65
70
75
July mean temperature (F)
80
2
1
0.6
0.5
0.4
0.3
0.1
0.2
0.1
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Sand shiner
25
0.3
20
0.25
0.2
15
0.15
0.1
10
0.05
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
0.6
0.5
0.4
0.3
0.1
0.2
0.1
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Mimic shiner
25
0.3
20
0.25
0.2
15
0.15
0.1
10
0.05
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
7
6
5
4
3
0.1
2
1
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Bluntnose minnow
2
25
3.5
3
20
2.5
2
15
1.5
1
10
0.5
0
5
55
4000
2
Catchment Area (mi )
39
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Fathead minnow
0.3
0.25
0.2
0.15
0.1
0.1
0.05
0
0.01
10
100
1000
25
0.14
0.12
20
0.1
0.08
15
0.06
0.04
10
0.02
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Central stoneroller
9
8
7
6
5
4
3
0.1
2
1
0
0.01
10
100
1000
25
3
20
2.5
2
15
1.5
1
10
0.5
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Creek chub
2
60
50
40
30
0.1
20
10
0
0.01
10
100
1000
25
25
20
20
15
15
10
5
10
0
5
55
4000
2
Catchment Area (mi )
40
60
65
70
75
July mean temperature (F)
80
2
1
6
5
4
3
0.1
2
1
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Hornyhead chub
25
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
20
15
10
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
1.8
1.6
1.4
1.2
1
0.8
0.6
0.1
0.4
0.2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
River chub
25
3
20
2.5
2
15
1.5
1
10
0.5
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
400
350
300
250
200
0.1
150
100
50
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Common carp
2
25
160
140
20
120
100
15
80
60
40
10
20
0
5
55
4000
2
Catchment Area (mi )
41
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Blacknose dace
9
8
7
6
5
4
0.1
3
2
1
0
0.01
10
100
1000
25
10
9
8
7
6
5
4
3
2
1
0
20
15
10
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Longnose dace
1.2
1
0.8
0.6
0.1
0.4
0.2
0
0.01
10
100
1000
25
3
20
2.5
2
15
1.5
1
10
0.5
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Northern redbelly dace
2
0.4
0.35
0.3
0.25
0.2
0.1
0.15
0.1
0.05
0
0.01
10
100
1000
25
0.3
20
0.25
0.2
15
0.15
0.1
10
0.05
0
5
55
4000
2
Catchment Area (mi )
42
60
65
70
75
July mean temperature (F)
80
2
1
10
9
8
7
6
5
4
3
2
1
0
0.1
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Quillback
25
8
7
20
6
5
15
4
3
2
10
1
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
60
50
40
30
0.1
20
10
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
White sucker
25
45
40
20
35
30
15
25
20
15
10
10
5
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
0.08
0.07
0.06
0.05
0.04
0.1
0.03
0.02
0.01
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Lake chubsucker
2
25
0.35
0.3
20
0.25
0.2
15
0.15
0.1
10
0.05
0
5
55
4000
2
Catchment Area (mi )
43
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Northern hog sucker
40
35
30
25
20
0.1
15
10
5
0
25
20
18
16
14
12
10
8
6
4
2
0
20
15
10
5
0.01
10
100
1000
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Spotted sucker
1.8
1.6
1.4
1.2
1
0.8
0.6
0.1
0.4
0.2
0
0.01
25
1.8
1.6
20
1.4
1.2
1
15
0.8
0.6
0.4
10
0.2
0
5
10
100
1000
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Silver redhorse
2
6
5
4
3
0.1
2
1
0
0.01
10
100
1000
25
2.5
20
2
1.5
15
1
0.5
10
0
5
55
4000
2
Catchment Area (mi )
44
60
65
70
75
July mean temperature (F)
80
1
25
20
15
0.1
10
5
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Black redhorse
2
25
12
20
10
8
15
6
4
10
2
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
90
80
70
60
50
40
30
0.1
20
10
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Golden redhorse
25
45
40
20
35
30
25
15
20
15
10
10
5
0
5
4000
55
2
Shorthead redhorse
2
1
50
45
40
35
30
25
20
15
10
5
0
0.1
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Catchment Area (mi )
Catchment Area (mi )
45
80
25
25
20
20
15
15
10
5
10
0
5
55
4000
2
60
65
70
75
July mean temperature (F)
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Greater redhorse
25
20
15
0.1
10
5
0
0.01
10
100
1000
25
10
9
8
7
6
5
4
3
2
1
0
20
15
10
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Black bullhead
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
0.1
0.01
10
100
1000
25
6
20
5
4
15
3
2
10
1
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Brown bullhead
2
1.2
1
0.8
0.6
0.1
0.4
0.2
0
0.01
10
100
1000
25
1.4
1.2
20
1
0.8
15
0.6
0.4
10
0.2
0
5
55
4000
2
Catchment Area (mi )
46
60
65
70
75
July mean temperature (F)
80
1
6
5
4
3
0.1
2
1
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Yellow bullhead
2
25
7
6
20
5
4
15
3
2
10
1
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
14
12
10
8
6
0.1
4
2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Channel catfish
25
14
12
20
10
8
15
6
4
10
2
0
5
4000
55
2
Stonecat
2
1
18
16
14
12
10
8
0.1
6
4
2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Catchment Area (mi )
Catchment Area (mi )
47
80
25
20
18
16
14
12
10
8
6
4
2
0
20
15
10
5
55
4000
2
60
65
70
75
July mean temperature (F)
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Tadpole madtom
3
2.5
2
1.5
0.1
1
0.5
0
0.01
10
100
1000
25
0.6
20
0.5
0.4
15
0.3
0.2
10
0.1
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Flathead catfish
12
10
8
6
0.1
4
2
0
0.01
10
100
1000
25
3
20
2.5
2
15
1.5
1
10
0.5
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Grass pickerel
2
1.2
1
0.8
0.6
0.1
0.4
0.2
0
0.01
10
100
1000
25
1.4
1.2
20
1
0.8
15
0.6
0.4
10
0.2
0
5
55
4000
2
Catchment Area (mi )
48
60
65
70
75
July mean temperature (F)
80
1
14
12
10
8
6
0.1
4
2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Northern pike
2
25
7
6
20
5
4
15
3
2
10
1
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
18
16
14
12
10
8
6
0.1
4
2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Brook trout
25
35
30
20
25
20
15
15
10
10
5
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
25
20
15
0.1
10
5
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Brown trout
2
25
45
40
20
35
30
25
15
20
15
10
10
5
0
5
55
4000
2
Catchment Area (mi )
49
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Coho salmon
0.1
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
0.1
0.01
10
100
1000
25
0.12
20
0.1
0.08
15
0.06
0.04
10
0.02
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Rainbow trout
8
7
6
5
4
0.1
3
2
1
0
0.01
10
100
1000
25
4.5
4
20
3.5
3
2.5
15
2
1.5
1
10
0.5
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Chinook salmon
2
0.6
0.5
0.4
0.3
0.1
0.2
0.1
0
0.01
10
100
1000
25
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
20
15
10
5
55
4000
2
Catchment Area (mi )
50
60
65
70
75
July mean temperature (F)
80
2
1
14
12
10
8
6
0.1
4
2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Mottled sculpin
25
8
7
20
6
5
15
4
3
2
10
1
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
1.4
1.2
1
0.8
0.6
0.1
0.4
0.2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Slimy sculpin
25
3
20
2.5
2
15
1.5
1
10
0.5
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
18
16
14
12
10
8
0.1
6
4
2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Rock bass
2
25
16
14
20
12
10
15
8
6
4
10
2
0
5
55
4000
2
Catchment Area (mi )
51
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Green sunfish
6
5
4
3
0.1
2
1
0
0.01
10
100
1000
25
3.5
3
20
2.5
2
15
1.5
1
10
0.5
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Bluegill
3.5
3
2.5
2
1.5
0.1
1
0.5
0
0.01
10
100
1000
25
2.5
20
2
1.5
15
1
0.5
10
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Longear sunfish
2
1.2
1
0.8
0.6
0.1
0.4
0.2
0
0.01
10
100
1000
25
0.6
20
0.5
0.4
15
0.3
0.2
10
0.1
0
5
55
4000
2
Catchment Area (mi )
52
60
65
70
75
July mean temperature (F)
80
2
1
6
5
4
3
0.1
2
1
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Pumpkinseed
25
3.5
3
20
2.5
2
15
1.5
1
10
0.5
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
12
10
8
6
0.1
4
2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Smallmouth bass
25
8
7
20
6
5
15
4
3
2
10
1
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
2.5
2
1.5
0.1
1
0.5
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Largemouth bass
2
25
1.8
1.6
20
1.4
1.2
1
15
0.8
0.6
0.4
10
0.2
0
5
55
4000
2
Catchment Area (mi )
53
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
White crappie
3.5
3
2.5
2
1.5
0.1
1
0.5
0
0.01
10
100
1000
25
0.4
0.35
20
0.3
0.25
15
0.2
0.15
0.1
10
0.05
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Black crappie
3
2.5
2
1.5
0.1
1
0.5
0
0.01
10
100
1000
25
1.2
20
1
0.8
15
0.6
0.4
10
0.2
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Walleye
2
3.5
3
2.5
2
1.5
0.1
1
0.5
0
0.01
10
100
1000
25
3
20
2.5
2
15
1.5
1
10
0.5
0
5
55
4000
2
Catchment Area (mi )
54
60
65
70
75
July mean temperature (F)
80
1
0.6
0.5
0.4
0.3
0.1
0.2
0.1
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Yellow perch
2
25
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
20
15
10
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
1.6
1.4
1.2
1
0.8
0.1
0.6
0.4
0.2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Logperch
25
0.9
0.8
20
0.7
0.6
0.5
15
0.4
0.3
0.2
10
0.1
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
1.4
1.2
1
0.8
0.6
0.1
0.4
0.2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Blackside darter
2
25
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
20
15
10
5
55
4000
2
Catchment Area (mi )
55
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Greenside darter
0.4
0.35
0.3
0.25
0.2
0.1
0.15
0.1
0.05
0
0.01
10
100
1000
25
0.3
20
0.25
0.2
15
0.15
0.1
10
0.05
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Rainbow darter
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.1
0.01
10
100
1000
25
0.9
0.8
20
0.7
0.6
0.5
15
0.4
0.3
0.2
10
0.1
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Johnny darter
2
2.5
2
1.5
0.1
1
0.5
0
0.01
10
100
1000
25
1.2
20
1
0.8
15
0.6
0.4
10
0.2
0
5
55
4000
2
Catchment Area (mi )
56
60
65
70
75
July mean temperature (F)
80
2
1
2.5
2
1.5
0.1
1
0.5
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Bowfin
25
1.2
20
1
0.8
15
0.6
0.4
10
0.2
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
60
50
40
30
0.1
20
10
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Gizzard shad
25
16
14
20
12
10
15
8
6
4
10
2
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
14
12
10
8
6
0.1
4
2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Central mudminnow
2
25
12
20
10
8
15
6
4
10
2
0
5
55
4000
2
Catchment Area (mi )
57
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Pirate perch
3.5
3
2.5
2
1.5
0.1
1
0.5
0
0.01
10
100
1000
25
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
20
15
10
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Burbot
3
2.5
2
1.5
0.1
1
0.5
0
0.01
10
100
1000
25
3
20
2.5
2
15
1.5
1
10
0.5
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Brook silverside
2
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0.1
0.01
10
100
1000
25
0.25
20
0.2
0.15
15
0.1
0.05
10
0
5
55
4000
2
Catchment Area (mi )
58
60
65
70
75
July mean temperature (F)
80
2
1
0.45
0.4
0.35
0.3
0.25
0.2
0.1
0.15
0.1
0.05
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Brook stickleback
25
0.4
0.35
20
0.3
0.25
15
0.2
0.15
0.1
10
0.05
0
5
55
4000
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
2
1
1.6
1.4
1.2
1
0.8
0.1
0.6
0.4
0.2
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Hybrid sunfish
25
1.2
20
1
0.8
15
0.6
0.4
10
0.2
0
5
4000
55
2
Catchment Area (mi )
60
65
70
75
July mean temperature (F)
80
1
80
70
60
50
40
0.1
30
20
10
0
0.01
10
100
1000
July weekly temperature range (F)
2
90% Exceedence flow yield (cfs/mi )
Freshwater drum
2
25
2.5
20
2
1.5
15
1
0.5
10
0
5
55
4000
2
Catchment Area (mi )
59
60
65
70
75
July mean temperature (F)
80
4. Numerical density (#/acre) vs. temperature
25
2500
800
Brook trout (#/acre)
July weekly temperature range (F)
Brook trout
700
20
600
500
15
400
300
200
10
2000
1500
1000
500
100
0
0
5
55
60
65
70
75
July mean temperature (F)
55
57
59
61
63
65
67
69
71
73
75
July mean temperature (F)
80
25
4000
500
450
400
350
300
250
200
150
100
50
0
20
15
10
Brown trout (#/acre)
July weekly temperature range (F)
Brown trout
60
65
70
75
July mean temperature (F)
July weekly temperature range (F)
Rainbow trout
25
300
250
200
15
150
100
10
50
0
5
55
60
65
70
75
July mean temperature (F)
2500
2000
1500
1000
0
80
20
3000
500
5
55
Unstocked
Stocked
3500
80
60
55
57
59
61
63
65
67
69
71
July mean temperature (F)
73
75